

'''simple docstring'''





import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[str]:
					if hor == 1_28:
										lowerCamelCase_  				=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
										lowerCamelCase_  				=      (32, 1_28, 2_56)
										lowerCamelCase_  				=      ('UpResnetBlock1D', 'UpResnetBlock1D')

					elif hor == 32:
										lowerCamelCase_  				=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
										lowerCamelCase_  				=      (32, 64, 1_28, 2_56)
										lowerCamelCase_  				=      ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
					lowerCamelCase_  				=      torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''	)
					lowerCamelCase_  				=      model.state_dict()
					lowerCamelCase_  				=      {
					    'down_block_types': down_block_types,
					    'block_out_channels': block_out_channels,
					    'up_block_types': up_block_types,
					    'layers_per_block': 1,
					    'use_timestep_embedding': True,
					    'out_block_type': 'OutConv1DBlock',
					    'norm_num_groups': 8,
					    'downsample_each_block': False,
					    'in_channels': 14,
					    'out_channels': 14,
					    'extra_in_channels': 0,
					    'time_embedding_type': 'positional',
					    'flip_sin_to_cos': False,
					    'freq_shift': 1,
					    'sample_size': 6_55_36,
					    'mid_block_type': 'MidResTemporalBlock1D',
					    'act_fn': 'mish',
					}
					lowerCamelCase_  				=      UNetaDModel(**__UpperCamelCase	)
					print(f'''length of state dict: {len(state_dict.keys()	)}'''	)
					print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()	)}'''	)
					lowerCamelCase_  				=      dict(zip(model.state_dict().keys()    ,hf_value_function.state_dict().keys()	)	)
					for k, v in mapping.items():
										lowerCamelCase_  				=      state_dict.pop(__UpperCamelCase	)
					hf_value_function.load_state_dict(__UpperCamelCase	)

					torch.save(hf_value_function.state_dict()    ,f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''	)
					with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''    ,'w'	) as f:
										json.dump(__UpperCamelCase    ,__UpperCamelCase	)







def   _UpperCamelCase	(		)       ->			Tuple:
					lowerCamelCase_  				=      {
					    'in_channels': 14,
					    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
					    'up_block_types': (),
					    'out_block_type': 'ValueFunction',
					    'mid_block_type': 'ValueFunctionMidBlock1D',
					    'block_out_channels': (32, 64, 1_28, 2_56),
					    'layers_per_block': 1,
					    'downsample_each_block': True,
					    'sample_size': 6_55_36,
					    'out_channels': 14,
					    'extra_in_channels': 0,
					    'time_embedding_type': 'positional',
					    'use_timestep_embedding': True,
					    'flip_sin_to_cos': False,
					    'freq_shift': 1,
					    'norm_num_groups': 8,
					    'act_fn': 'mish',
					}

					lowerCamelCase_  				=      torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'	)
					lowerCamelCase_  				=      model
					lowerCamelCase_  				=      UNetaDModel(**__UpperCamelCase	)
					print(f'''length of state dict: {len(state_dict.keys()	)}'''	)
					print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()	)}'''	)

					lowerCamelCase_  				=      dict(zip(state_dict.keys()    ,hf_value_function.state_dict().keys()	)	)
					for k, v in mapping.items():
										lowerCamelCase_  				=      state_dict.pop(__UpperCamelCase	)

					hf_value_function.load_state_dict(__UpperCamelCase	)

					torch.save(hf_value_function.state_dict()    ,'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'	)
					with open('hub/hopper-medium-v2/value_function/config.json'    ,'w'	) as f:
										json.dump(__UpperCamelCase    ,__UpperCamelCase	)


if __name__ == "__main__":
				unet(32)
				# unet(128)
				value_function()





import math

def a__       (			A_   ):






  '''simple docstring'''





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5,	int(math.sqrt(A_   ) + 1   ),	6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def a__       (			A_ = 0.1   ):






  '''simple docstring'''





  __magic_name__								=			3
  __magic_name__								=			3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1,	(j + 2) * (j + 2),	j + 1   ):
      primes += is_prime(A_   )
    j += 2
  return j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

